How to Use the Arize AI MCP in Cline

Q: Can Cline write code based on my Arize AI telemetry?

Yes, Cline reads your performance data using listspans and writes code to fix slow steps. It can edit your local prompt templates or Arize AI model configurations right in VS Code.

Q: Does Cline support running new Arize AI experiments?

Yes, Cline uses listexperiments to check your active Arize AI runs. It can compare those runs against your current local git branch to verify your changes.

Q: Can I use Cline to export raw Arize AI tracing data?

Yes, Cline can fetch raw trace details with listspans and write them to a local JSON file. This is perfect for offline debugging of your Arize AI models.

Tell Cline to build regression tests and fix prompt drift using your live Arize AI production traces.

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Connect Arize AI MCP to Cline

Create your Vinkius account to connect Arize AI to Cline and route execution through our secure gateway. The platform manages server hosting, runtime updates, and security layers. Configuration requires no manual server provisioning.

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Setup Arize AI with Cline

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Fix drifting models using Cline and Arize AI

When your production model starts drifting, Cline queries `get_model` to see which Arize AI features are failing. The agent analyzes the drift metrics and immediately locates the corresponding pre-processing code in your editor. It then writes a patch inside VS Code to normalize the incoming data. This MCP Server handles the data fetching so Cline can focus on writing the actual code to fix the Arize AI pipeline.

Build Arize AI test suites in Cline from live spans

Cline uses `list_spans` to grab actual Arize AI production inputs that caused your LLM to hallucinate. Instead of making up fake test cases, Cline writes real-world edge cases directly into your unit tests. The agent then uses `create_dataset` to register these test suites back into your Arize AI monitoring dashboard. Cline connects your live production telemetry directly to your local test suite.

Generate local Arize AI dashboards with Cline

Tell Cline to build an internal dashboard, and it will use `list_projects` and `list_datasets` to pull your active Arize AI monitoring metadata. It writes the React components, fetches the data, and sets up the views. You don't have to write any boilerplate API code in VS Code. Cline handles the entire integration by calling the tools exposed by this Arize AI MCP Server.

Setup guide

Set up Arize AI MCP in Cline

Prerequisites

VS Code with Cline extension installed
Active Vinkius subscription with a valid endpoint token

1

Open Cline MCP settings

Click the Cline icon in the VS Code sidebar to open the Cline panel. Then click the MCP Servers icon (server stack) at the top-right corner of the panel.
2

Add a remote server

Click "Remote Servers" at the top, then click "Add Remote MCP". In the Name field, type arize-ai-alternative-mcp. In the URL field, paste your Vinkius endpoint: https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp. Get your token from cloud.vinkius.com.
3

Enable the server

After saving, the server appears in the Cline MCP panel. Toggle the switch to enable it. The status indicator turns green when the connection is live.
4

Start using tools

Return to the Cline chat and ask: "Check my latest Arize AI refund status." Cline will discover the available tools and request your approval before invoking each one — giving you full control over every action.

Cline MCP Settings

{
  "mcpServers": {
    "arize-ai-alternative-mcp": {
      "url": "https://edge.vinkius.com/[YOUR_TOKEN_HERE]/mcp"
    }
  }
}

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Common questions about Arize AI MCP in Cline

Open the Cline sidebar and click the MCP icon to add this server. Once connected, Cline can run `list_datasets` to read your Arize AI evaluation data directly.

Yes, Cline reads your performance data using `list_spans` and writes code to fix slow steps. It can edit your local prompt templates or Arize AI model configurations right in VS Code.

Yes, Cline uses `list_experiments` to check your active Arize AI runs. It can compare those runs against your current local git branch to verify your changes.

Yes, Cline can fetch raw trace details with `list_spans` and write them to a local JSON file. This is perfect for offline debugging of your Arize AI models.

Absolutely. Your Arize AI model schemas, dataset records, and tracing spans are processed in an ephemeral, zero-trust sandbox. The data is never stored or used to train any models, ensuring your telemetry remains private inside Cline.

Use it with your favorite AI tools

Connect this server to Cursor, Claude, VS Code, and more.

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